from datetime import datetime
import pandas as pd
from pathlib import Path
import plotly
import plotly.express as px
import numpy as np
from statsmodels.tsa.api import VAR
import urllib.request
plotly.offline.init_notebook_mode()
NOW = datetime.now()
TODAY = NOW.date()
print('Aktualisiert:', NOW)
Aktualisiert: 2020-11-24 14:06:18.676027
STATE_NAMES = ['Burgenland', 'Kärnten', 'Niederösterreich',
'Oberösterreich', 'Salzburg', 'Steiermark',
'Tirol', 'Vorarlberg', 'Wien']
# TODO: Genauer recherchieren!
EVENTS = {'1. Lockdown': (np.datetime64('2020-03-20'), np.datetime64('2020-04-14'),
'red', 'inside top left'),
'1. Maskenpflicht': (np.datetime64('2020-03-30'), np.datetime64('2020-06-15'),
'yellow', 'inside bottom left'),
'2. Maskenpflicht': (np.datetime64('2020-07-24'), np.datetime64(TODAY),
'yellow', 'inside bottom left'),
'Soft Lockdown': (np.datetime64('2020-11-03'), np.datetime64('2020-11-17'),
'orange', 'inside top left'),
'2. Lockdown': (np.datetime64('2020-11-17'), np.datetime64(TODAY),
'red', 'inside top left')}
def load_data(URL, date_columns):
data_file = Path(URL).name
try:
# Only download the data if we don't have it, to avoid
# excessive server access during local development
with open(data_file):
print("Using local", data_file)
except FileNotFoundError:
print("Downloading", URL)
urllib.request.urlretrieve(URL, data_file)
return pd.read_csv(data_file, sep=';', parse_dates=date_columns, infer_datetime_format=True, dayfirst=True)
raw_data = load_data("https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv", [0])
additional_data = load_data("https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv", [0, 2])
Downloading https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv Downloading https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv
cases = raw_data.query("Bundesland == 'Österreich'")
cases.insert(0, 'AnzahlFaelle_avg7', cases.AnzahlFaelle7Tage / 7)
time = cases.Time
tests = additional_data.query("Bundesland == 'Alle'")
tests.insert(2, 'TagesTests', np.concatenate([[np.nan], np.diff(tests.TestGesamt)]))
tests.insert(3, 'TagesTests_avg7', np.concatenate([[np.nan] * 7, (tests.TestGesamt.values[7:] - tests.TestGesamt.values[:-7])/7]))
tests.insert(0, 'Time', tests.MeldeDatum)
fig = px.line(cases, x='Time', y=["AnzahlFaelle", "AnzahlFaelle_avg7"], log_y=True, title="Fallzahlen")
fig.add_scatter(x=tests.Time, y=tests.TagesTests, name='Tests')
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
all_data = tests.merge(cases, on='Time', how='outer')
all_data.insert(1, 'PosRate', all_data.AnzahlFaelle / all_data.TagesTests)
all_data.insert(1, 'PosRate_avg7', all_data.AnzahlFaelle_avg7 / all_data.TagesTests_avg7)
fig = px.line(all_data, x='Time', y=['PosRate', 'PosRate_avg7'], log_y=False, title="Anteil Positiver Tests")
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
states = []
rates = []
for state_name, state_data in raw_data.groupby('Bundesland'):
x = np.log2(state_data.AnzahlFaelle7Tage)
rate = 2**np.array(np.diff(x))
rates.append(rate)
states.append(state_name)
growth = pd.DataFrame({n: r for n, r in zip(states, rates)})
fig = px.line(growth, x=time[1:], y=STATE_NAMES, title='Wachstumsrate')
fig.update_layout(yaxis=dict(range=[0.25, 4]))
fig.show()
/usr/share/miniconda/lib/python3.8/site-packages/pandas/core/series.py:726: RuntimeWarning: divide by zero encountered in log2 /usr/share/miniconda/lib/python3.8/site-packages/numpy/lib/function_base.py:1280: RuntimeWarning: invalid value encountered in subtract
model = VAR(growth[150:][STATE_NAMES])
res = model.fit(1)
res.summary()
Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Tue, 24, Nov, 2020
Time: 14:06:22
--------------------------------------------------------------------
No. of Equations: 9.00000 BIC: -42.5883
Nobs: 120.000 HQIC: -43.8299
Log likelihood: 1238.28 FPE: 3.96018e-20
AIC: -44.6789 Det(Omega_mle): 1.92688e-20
--------------------------------------------------------------------
Results for equation Burgenland
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.705421 0.208031 3.391 0.001
L1.Burgenland 0.128971 0.091422 1.411 0.158
L1.Kärnten -0.310629 0.076937 -4.037 0.000
L1.Niederösterreich 0.009563 0.220167 0.043 0.965
L1.Oberösterreich 0.272506 0.180486 1.510 0.131
L1.Salzburg 0.128191 0.089814 1.427 0.153
L1.Steiermark 0.085383 0.128369 0.665 0.506
L1.Tirol 0.165611 0.084895 1.951 0.051
L1.Vorarlberg 0.010402 0.084086 0.124 0.902
L1.Wien -0.160967 0.173590 -0.927 0.354
======================================================================================
Results for equation Kärnten
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.699306 0.266180 2.627 0.009
L1.Burgenland -0.011724 0.116977 -0.100 0.920
L1.Kärnten 0.347627 0.098443 3.531 0.000
L1.Niederösterreich 0.091511 0.281709 0.325 0.745
L1.Oberösterreich -0.216481 0.230936 -0.937 0.349
L1.Salzburg 0.165122 0.114919 1.437 0.151
L1.Steiermark 0.191398 0.164251 1.165 0.244
L1.Tirol 0.133774 0.108625 1.232 0.218
L1.Vorarlberg 0.197297 0.107590 1.834 0.067
L1.Wien -0.568310 0.222112 -2.559 0.011
======================================================================================
Results for equation Niederösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.341036 0.088992 3.832 0.000
L1.Burgenland 0.106062 0.039109 2.712 0.007
L1.Kärnten -0.027898 0.032913 -0.848 0.397
L1.Niederösterreich 0.133316 0.094184 1.415 0.157
L1.Oberösterreich 0.268452 0.077209 3.477 0.001
L1.Salzburg -0.006897 0.038421 -0.180 0.858
L1.Steiermark -0.060781 0.054914 -1.107 0.268
L1.Tirol 0.096333 0.036317 2.653 0.008
L1.Vorarlberg 0.147240 0.035971 4.093 0.000
L1.Wien 0.012484 0.074259 0.168 0.866
======================================================================================
Results for equation Oberösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.215203 0.105504 2.040 0.041
L1.Burgenland 0.005722 0.046365 0.123 0.902
L1.Kärnten 0.034296 0.039019 0.879 0.379
L1.Niederösterreich 0.089105 0.111659 0.798 0.425
L1.Oberösterreich 0.349924 0.091534 3.823 0.000
L1.Salzburg 0.086204 0.045550 1.893 0.058
L1.Steiermark 0.195059 0.065103 2.996 0.003
L1.Tirol 0.027252 0.043055 0.633 0.527
L1.Vorarlberg 0.111690 0.042644 2.619 0.009
L1.Wien -0.114185 0.088037 -1.297 0.195
======================================================================================
Results for equation Salzburg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.862178 0.229264 3.761 0.000
L1.Burgenland 0.053311 0.100753 0.529 0.597
L1.Kärnten -0.017958 0.084790 -0.212 0.832
L1.Niederösterreich -0.115189 0.242640 -0.475 0.635
L1.Oberösterreich 0.047230 0.198908 0.237 0.812
L1.Salzburg 0.043863 0.098981 0.443 0.658
L1.Steiermark 0.113253 0.141471 0.801 0.423
L1.Tirol 0.233188 0.093560 2.492 0.013
L1.Vorarlberg 0.031132 0.092668 0.336 0.737
L1.Wien -0.218236 0.191308 -1.141 0.254
======================================================================================
Results for equation Steiermark
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.192062 0.157018 1.223 0.221
L1.Burgenland -0.040469 0.069004 -0.586 0.558
L1.Kärnten -0.010440 0.058071 -0.180 0.857
L1.Niederösterreich 0.201805 0.166178 1.214 0.225
L1.Oberösterreich 0.394823 0.136227 2.898 0.004
L1.Salzburg -0.039291 0.067790 -0.580 0.562
L1.Steiermark -0.054042 0.096890 -0.558 0.577
L1.Tirol 0.195829 0.064077 3.056 0.002
L1.Vorarlberg 0.054343 0.063466 0.856 0.392
L1.Wien 0.116289 0.131023 0.888 0.375
======================================================================================
Results for equation Tirol
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.316098 0.200140 1.579 0.114
L1.Burgenland 0.066072 0.087954 0.751 0.453
L1.Kärnten -0.080472 0.074019 -1.087 0.277
L1.Niederösterreich -0.121149 0.211816 -0.572 0.567
L1.Oberösterreich -0.123052 0.173640 -0.709 0.479
L1.Salzburg -0.000101 0.086407 -0.001 0.999
L1.Steiermark 0.378864 0.123499 3.068 0.002
L1.Tirol 0.536430 0.081674 6.568 0.000
L1.Vorarlberg 0.227618 0.080896 2.814 0.005
L1.Wien -0.189908 0.167005 -1.137 0.255
======================================================================================
Results for equation Vorarlberg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.179262 0.229650 0.781 0.435
L1.Burgenland 0.013689 0.100923 0.136 0.892
L1.Kärnten -0.068684 0.084933 -0.809 0.419
L1.Niederösterreich 0.239448 0.243048 0.985 0.325
L1.Oberösterreich 0.009650 0.199243 0.048 0.961
L1.Salzburg 0.231601 0.099148 2.336 0.019
L1.Steiermark 0.156184 0.141709 1.102 0.270
L1.Tirol 0.051656 0.093717 0.551 0.582
L1.Vorarlberg 0.003964 0.092824 0.043 0.966
L1.Wien 0.198419 0.191630 1.035 0.300
======================================================================================
Results for equation Wien
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.665837 0.127158 5.236 0.000
L1.Burgenland -0.011942 0.055882 -0.214 0.831
L1.Kärnten -0.011361 0.047028 -0.242 0.809
L1.Niederösterreich -0.059767 0.134577 -0.444 0.657
L1.Oberösterreich 0.267255 0.110321 2.423 0.015
L1.Salzburg 0.007650 0.054899 0.139 0.889
L1.Steiermark 0.005152 0.078465 0.066 0.948
L1.Tirol 0.078740 0.051892 1.517 0.129
L1.Vorarlberg 0.185020 0.051397 3.600 0.000
L1.Wien -0.114559 0.106106 -1.080 0.280
======================================================================================
Correlation matrix of residuals
Burgenland Kärnten Niederösterreich Oberösterreich Salzburg Steiermark Tirol Vorarlberg Wien
Burgenland 1.000000 0.080727 -0.062436 0.193132 0.233693 0.014940 0.067166 -0.139510 0.095232
Kärnten 0.080727 1.000000 -0.065457 0.173077 0.068909 -0.162849 0.174267 0.009943 0.277787
Niederösterreich -0.062436 -0.065457 1.000000 0.225352 0.055236 0.143019 0.071641 0.045809 0.354013
Oberösterreich 0.193132 0.173077 0.225352 1.000000 0.238988 0.263145 0.066431 0.052723 0.026483
Salzburg 0.233693 0.068909 0.055236 0.238988 1.000000 0.136102 0.041565 0.067202 -0.075394
Steiermark 0.014940 -0.162849 0.143019 0.263145 0.136102 1.000000 0.094926 0.093861 -0.205122
Tirol 0.067166 0.174267 0.071641 0.066431 0.041565 0.094926 1.000000 0.132331 0.085212
Vorarlberg -0.139510 0.009943 0.045809 0.052723 0.067202 0.093861 0.132331 1.000000 0.067774
Wien 0.095232 0.277787 0.354013 0.026483 -0.075394 -0.205122 0.085212 0.067774 1.000000